Multiwavelet feature sets for ECG beat classification

Sarvan, Cagla; Özkurt, Nalan

doi:10.1109/siu.2017.7960297

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Many tools have been used to extract features from ECG signals, such as wavelet transform ( 113 ), PCA ( 125 ), statistics ( 126 ), analysis-based autocorrelation ( 127 ), Fourier transform ( 128 ), singular value decomposition SVD, variational mode decomposition VMD ( 129 ), Hilbert transform ( 130 ), and morphological methods ( 131 ). Several features have been extracted ( 132 ), for example, morphological features (commonly P, Q, R, S, T, and U waves) ( 121 , 126 , 133 ), statistical features (energy, mean, standard deviation, maximum, minimum, kurtosis and skewness) ( 126 ), wavelet features (coefficients and metrics extracted from continuous WT, Dual-Tree complex WT, tunable Q factor WT, flexible analytic WT and dyadic DWT) ( 134 – 136 ) and others, such as Lyapunov Exponents ( 137 ), the ratio of power spectrum ( 138 ), power spectral density ( 138 ), Kolmogorov Sinai entropy ( 137 ), and Kolmogorov complexity ( 137 ).…”

Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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“…In [3], method block-based neural networks have been applied and optimized, working for ECG heartbeat cases pattern classification, and compared to our proposed work, a CNN configured with different sets of batch size and learning rate. 1-D CNN method has been applied for classification ECG classes in [4], compared to our work, 1D feature has been converted to 2D image spectrogram based on CWT. In [5], a new SoftMax layer has been added over the hidden layer, which is deep neural network method, where the labelling for classes of ECG in the The proposed work focuses on increasing the performance during the classification of the ECG signals.…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 compares three state-of-the-art with our proposed method and shows that the proposed method has the highest accuracy. For instance, in [4], proposed a method during feature extraction (average, mean, standard From the experimental works and results during the evaluations phase, we can have concluded that at batch size 250 and learning rate set to 0.001, the model selected of 2D-CNN achieved high accuracy. (65536).…”

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confidence: 99%

A Novel Approach for ECG Classification Using Probability Continuous Wavelet Transform and Alexnet-Deep Neural Network

2022

IJIES

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Arrhythmia identification using electrocardiograms (ECGs) is a critical component of biomedical signal processing and pattern recognition. The classification of ECG arrhythmias is presented in this article using features extracted from the Extreme Value distributor of the probability density function (PDF), also training model built using the Alexnet architecture of a conventional neural network (CNN). A continuous wavelet transform (CWT) was used to convert the features space to a two-dimensional image. Additionally, a comparison of the two approaches was conducted. In the first case, we used CWT to convert ECG data to two-dimensional images. In the second case, features were extracted from PDF files and converted to the image domain using a CWT. With a learning rate of 0.001 and batch size of 250, the accuracy of tests was 98.67 %. The results indicated that the proposed method is more accurate than the conventional method when it comes to feature extraction.

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Multi-objective advisory system for arrhytmia classification

Sarvan

Özkurt

2021

Biomedical Signal Processing and Control

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Multiwavelet feature sets for ECG beat classification

Cited by 4 publications

References 12 publications

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Novel Approach for ECG Classification Using Probability Continuous Wavelet Transform and Alexnet-Deep Neural Network

Multi-objective advisory system for arrhytmia classification

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